Validation Assessment

This page discusses Validation Assessment, which focuses on
the methods for the Validation
of a CFD codes for simulation of a certain type of flows.

Validation

Validation is defined as:

The process of determining the degree to which a model
is an accurate representation of the real world from the perspective of
the intended uses of the model. (AIAA G-077-1998)

Validation has also been described as "solving the right
equations". It is not possible to validate the entire CFD code.
One can only validate the code for a specific range of applications for
which there is experimental data. Thus one validates a model or
simulation. Applying the code to flows beyond the region of validity
is termed prediction.

Validation examines if the conceptual models, computational models
as implemented into the CFD code, and computational simulation agree
with real world observations. The strategy is to indentify and
quantify error and uncertainty through comparison of simulation results
with experimental data. The experiment data sets themselves will
contain bias errors and random errors which must be properly quantified
and documented as part of the data set. The accuracy required in the
validation activities is dependent on the application, and so, the
validation should be flexible to allow various levels of accuracy.

The approach to Validation Assessment is to perform a systematic
comparison of CFD simulation results to experimental data from a
set increasingly complex cases.

Each CFD simulation requires verification of the calculation as
specified in the discussion of Verification
Assessment.

Validation Assessment Process

The process for Validation Assessment of a CFD simulation
can be summarized as:

One should check for consistency in the CFD solution. For example, the
flow in a duct should maintain mass conservation through the duct. Further
total pressure recovery in an inlet should stay constant or decrease through
the duct.

The CFD simulation results should demonstrate temporal convergence.
Further details and methods can be found on the page entitled Examining Temporal Convergence.

5. Compare CFD Results to Experimental
Data.

Experimental data is the observation of the "real world" in
some controlled manner. By comparing the CFD results to experimental data,
one hopes that there is a good agreement, which inreases confidence that
the physical models and the code represents the "real world" for this class
of simulations. However, the experimental data contains some level of error.
This is usually related to the complexity of the experiment. Validation
assessment calls for a "building block" approach of experiments which
sets a hierarchy of experiment complexity.

6. Examine Model Uncertainties.

The physical models in the CFD code contain uncertainties due to a lack
of complete understanding or knowledge of the physical processes. One of
the models with the most uncertainty is the turbulence models. The
uncertainty can be examined by running a number of simulations with the
various turbulence models and examine the affect on the results.

Building-Block Approach for Experiments

A building-block approach is followed in performing the validation
assessment for a complex system such as an aircraft inlet. The
approach consists of phases involving successively more complex flow
physics, geometry, and interactions. These phases include:

Unit Problems involve simple geometry, one element
of the complex flow physics, and one relevant flow feature. An
example is the measurement of a turbulent boundary layer over a flat
plate. The experiment data set contains detailed data collected with
high accuracy. The boundary conditions and initial conditions are
accurately measured.

Benchmark Cases involve fairly simple hardware
representing a key feature of the system. The flow field contains only
two separate flow features of the flow physics which are likely
coupled. An example is a shock / boundary layer interaction. The
experiment data set is extensive in scope and uncertainties are low;
however, some measurements, such as, initial and boundary conditions,
may not have been collected.

Subsystem Cases involve geometry of a component of
the complete system which may have been simplified. The flow physics
of the complete system may be well represented; but the level of
coupling between flow phenomena is typically reduced. An example is a
test of a subsonic diffuser for a supersonic inlet. The exact inflow
conditions may not be matched. The quality and quantity of the
experiment data set may not be as extensive as the benchmark cases.

Complete System Cases involve actual hardware and
the complete flow physics. All of the relevant flow features are
present. An example is a test of a mixed-compression inlet in the
10x10 wind tunnel at NASA Glenn. Less detailed data is collected since
the emphasis is on system evaluation. Uncertainties on initial and
boundary conditions may be large.

Requirements for Experimental Data

The experimental data likely has uncertainties and error associated
with it. In comparing the CFD simulation results to experimental data,
one should discuss the experimental errors. Plots comparing CFD results
and experimental data should include a visual display of the error bars
on the experimental data.